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Record W2159959807 · doi:10.34989/swp-2008-14

Driving Forces of the Canadian Economy: An Accounting Exercise

2021· preprint· en· W2159959807 on OpenAlexaffabout
Simona E. Cociuba, Alexander Ueberfeldt

Bibliographic record

VenueRePEc: Research Papers in Economics · 2021
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policy and Economic Growth
Canadian institutionsBank of Canada
Fundersnot available
KeywordsEconomicsProductivityGrowth accountingWageTotal factor productivityLabour economicsNational accountsMacroeconomics

Abstract

fetched live from OpenAlex

This paper analyses the Canadian economy for the post 1960 period. It uses an accounting procedure developed in Chari, Kehoe, and McGrattan (2006). The procedure identifies accounting factors that help align the predictions of the neoclassical growth model with macroeconomic variables observed in the data. The paper finds that total factor productivity and the consumptionleisure trade-off – the productivity and labor factors – are key to understanding the changes in output, labor supply and labor productivity observed in the Canadian economy. The paper performs a decomposition of the labor factor for Canada and the United States. It finds that the decline in the gender wage gap is a major driving force of the decrease in the labor market distortions. Moreover, the milder reduction in the labor market distortions observed in Canada, compared to the US, is due to a relative increase in effective labor taxes in Canada.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.061
Threshold uncertainty score0.444

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0070.011
Science and technology studies0.0040.001
Scholarly communication0.0040.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.042
GPT teacher head0.274
Teacher spread0.232 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations8
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueRePEc: Research Papers in EconomicsSame topicFiscal Policy and Economic GrowthFrench-language works237,207